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Agreement Assessment of Visual Interpretation and Digital Classification for Mapping Urban Landscape Heterogeneity Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso.

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Presentation on theme: "Agreement Assessment of Visual Interpretation and Digital Classification for Mapping Urban Landscape Heterogeneity Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso."— Presentation transcript:

1 Agreement Assessment of Visual Interpretation and Digital Classification for Mapping Urban Landscape Heterogeneity Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso 2008 BES Annual Meeting

2 Motivations (1) Visual interpretation of remotely sensed images is extensively used for urban analysis. – Patch mapping – Patch classification: Within-patch composition estimation. However, few studies evaluate accuracy of the within-patch composition estimations, particularly in urban settings.

3 Motivations (2) Digital classification of high resolution image – Object-based classification greatly increases the accuracy of digital classification – Digital classification as reference data

4 Visual interpretation Cadenasso et al., 2007

5 Digital classification

6 Research Questions What is the relative agreement of percent cover estimation between the two methods? What are the spatial patterns of the patches with large disagreement?

7 # of HERCULES patches: 2250 Degree of Disagreement Digital Classification Visual Interpretation

8 What is the relative agreement between the two classification methods? Standard Procedure: the strictest agreement assessment method Plus-one Method: A modification of the standard procedure, and accepts plus or minus one class of the actual class as agreement Fuzzy Set Theory: Create fuzzy rules to account for fuzzy class boundaries.

9 Results: Overall Agreement CVFVBare SoilPaveBuild Standard61.1%55.4%80.8%47.5%70.6% Plus-one98.6%96.4%97.1%97.2%98.2% Fuzzy81.6%75.3%93.8%63.3%84.8%

10 Observations from Agreement Assessment Largest agreement when patches were dominated by one type of cover Largest disagreement for cover ranges %, 35-75% Vegetation: underestimated when cover <35% Pavement: Underestimated Buildings: Overestimated.

11 Do the patches with large disagreement cluster spatially in the watershed? Getis-Ord General G index: test whether the patches with large disagreement tend to cluster spatially Anselin local Moran’s I index: detect spatial clusters (i.e., hot spots) of large disagreement within the watershed.

12 Pattern Analysis: General G Index Except for fine vegetation, patches with large disagreement clustered spatially (p<0.05). Landscape FeaturesObserver Gp-value CV FV Bare soil <0.01 Pavement <0.01 Buildings <0.01

13 CV FV Bare Soil Pave Building

14 Next step: How does patch heterogeneity affect the degree of disagreement? Patch complexity metrics: e.g. patch size, shape, etc. Within-patch heterogeneity metrics – Patch composition – Within-patch configuration

15 Acknowledgements This research was funded by the National Science Foundation LTER program (grant DEB ) and biocomplexity program (grant BCE ). Many thanks to a lot of BES people and colleagues in the Cadenasso lab.

16 Questions & comments?


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